



'''simple docstring'''



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 						lowerCamelCase_				:







						def __init__(	self :   int					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[Any]=13					,     lowerCAmelCase__ :   List[str]=32					,     lowerCAmelCase__ :   Optional[Any]=3					,     lowerCAmelCase__ :   List[str]=4					,     lowerCAmelCase__ :   Dict=[10, 20, 30, 40]					,     lowerCAmelCase__ :   Dict=[2, 2, 3, 2]					,     lowerCAmelCase__ :   str=True					,     lowerCAmelCase__ :   Any=True					,     lowerCAmelCase__ :   Any=37					,     lowerCAmelCase__ :   Dict="gelu"					,     lowerCAmelCase__ :   Optional[Any]=10					,     lowerCAmelCase__ :   int=0.02					,     lowerCAmelCase__ :   Any=["stage2", "stage3", "stage4"]					,     lowerCAmelCase__ :   Tuple=[2, 3, 4]					,     lowerCAmelCase__ :   str=None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parent
													SCREAMING_SNAKE_CASE       :   List[Any]         =      batch_size
													SCREAMING_SNAKE_CASE       :   int         =      image_size
													SCREAMING_SNAKE_CASE       :   Any         =      num_channels
													SCREAMING_SNAKE_CASE       :   int         =      num_stages
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      hidden_sizes
													SCREAMING_SNAKE_CASE       :   List[Any]         =      depths
													SCREAMING_SNAKE_CASE       :   List[str]         =      is_training
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      use_labels
													SCREAMING_SNAKE_CASE       :   Tuple         =      intermediate_size
													SCREAMING_SNAKE_CASE       :   Any         =      hidden_act
													SCREAMING_SNAKE_CASE       :   int         =      num_labels
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      initializer_range
													SCREAMING_SNAKE_CASE       :   List[str]         =      out_features
													SCREAMING_SNAKE_CASE       :   List[Any]         =      out_indices
													SCREAMING_SNAKE_CASE       :   Dict         =      scope







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE       :   int         =      ids_tensor([self.batch_size]					,     self.num_labels					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.get_config()

													return config, pixel_values, labels







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													return ConvNextVaConfig(
													    num_channels=self.num_channels					,     hidden_sizes=self.hidden_sizes					,     depths=self.depths					,     num_stages=self.num_stages					,     hidden_act=self.hidden_act					,     is_decoder=lowerCAmelCase__					,     initializer_range=self.initializer_range					,     out_features=self.out_features					,     out_indices=self.out_indices					,     num_labels=self.num_labels					,     )







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      ConvNextVaModel(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					)
													# expected last hidden states: B, C, H // 32, W // 32
													self.parent.assertEqual(
													    result.last_hidden_state.shape					,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,     )







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ConvNextVaForImageClassification(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(lowerCAmelCase__					,     labels=lowerCAmelCase__					)
													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)					)







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      ConvNextVaBackbone(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Dict         =      model(lowerCAmelCase__					)

													# verify hidden states
													self.parent.assertEqual(len(result.feature_maps					)					,     len(config.out_features					)					)
													self.parent.assertListEqual(list(result.feature_maps[0].shape					)					,     [self.batch_size, self.hidden_sizes[1], 4, 4]					)

													# verify channels
													self.parent.assertEqual(len(model.channels					)					,     len(config.out_features					)					)
													self.parent.assertListEqual(model.channels					,     config.hidden_sizes[1:]					)

													# verify backbone works with out_features=None
													SCREAMING_SNAKE_CASE       :   int         =      None
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      ConvNextVaBackbone(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Tuple         =      model(lowerCAmelCase__					)

													# verify feature maps
													self.parent.assertEqual(len(result.feature_maps					)					,     1					)
													self.parent.assertListEqual(list(result.feature_maps[0].shape					)					,     [self.batch_size, self.hidden_sizes[-1], 1, 1]					)

													# verify channels
													self.parent.assertEqual(len(model.channels					)					,     1					)
													self.parent.assertListEqual(model.channels					,     [config.hidden_sizes[-1]]					)







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      config_and_inputs
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {'''pixel_values''': pixel_values}
													return config, inputs_dict







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      config_and_inputs
													SCREAMING_SNAKE_CASE       :   int         =      {'''pixel_values''': pixel_values, '''labels''': labels}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase_				(       snake_case_     ,					snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : Optional[Any]						  =					(
						    (
						        ConvNextVaModel,
						        ConvNextVaForImageClassification,
						        ConvNextVaBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase  : int						  =					(
						    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase  : Tuple						  =					False
						_lowerCAmelCase  : int						  =					False
						_lowerCAmelCase  : Dict						  =					False
						_lowerCAmelCase  : List[str]						  =					False
						_lowerCAmelCase  : Any						  =					False







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      ConvNextVaModelTester(self					)
													SCREAMING_SNAKE_CASE       :   Dict         =      ConfigTester(self					,     config_class=lowerCAmelCase__					,     has_text_modality=lowerCAmelCase__					,     hidden_size=37					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return







						@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''					)
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													pass







						@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''					)
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													pass







						@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''					)
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													pass







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													if not self.model_tester.is_training:
																				return

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      self.model_tester.prepare_config_and_inputs_with_labels()
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      True

																				if model_class.__name__ in [
																				    *get_values(lowerCAmelCase__					),
																				    *get_values(lowerCAmelCase__					),
																				]:
																											continue

																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.train()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					,     return_labels=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   int         =      model(**lowerCAmelCase__					).loss
																				loss.backward()







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													if not self.model_tester.is_training:
																				return

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      self.model_tester.prepare_config_and_inputs_with_labels()
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
																				SCREAMING_SNAKE_CASE       :   Any         =      True

																				if (
																				    model_class.__name__
																				    in [*get_values(lowerCAmelCase__					), *get_values(lowerCAmelCase__					)]
																				    or not model_class.supports_gradient_checkpointing
																				):
																											continue

																				SCREAMING_SNAKE_CASE       :   List[str]         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.gradient_checkpointing_enable()
																				model.train()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					,     return_labels=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Dict         =      model(**lowerCAmelCase__					).loss
																				loss.backward()







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      model_class(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Dict         =      inspect.signature(model.forward					)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				SCREAMING_SNAKE_CASE       :   str         =      [*signature.parameters.keys()]

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      ['''pixel_values''']
																				self.assertListEqual(arg_names[:1]					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													def check_hidden_states_output(lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   Tuple					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.eval()

																				with torch.no_grad():
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(**self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					)					)

																				SCREAMING_SNAKE_CASE       :   int         =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				SCREAMING_SNAKE_CASE       :   Tuple         =      self.model_tester.num_stages
																				self.assertEqual(len(lowerCAmelCase__					)					,     expected_num_stages + 1					)

																				# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-2:]					)					,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,     )

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   int         =      True
																				check_hidden_states_output(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      True

																				check_hidden_states_output(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__					)







						@slow
						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      ConvNextVaModel.from_pretrained(lowerCAmelCase__					)
																				self.assertIsNotNone(lowerCAmelCase__					)


def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   int         =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
							return image






@require_torch
@require_vision
class 						lowerCamelCase_				(       unittest.TestCase      ):







						@cached_property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''					) if is_vision_available() else None







						@slow
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''					).to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.default_image_processor
													SCREAMING_SNAKE_CASE       :   Dict         =      prepare_img()
													SCREAMING_SNAKE_CASE       :   int         =      preprocessor(images=lowerCAmelCase__					,     return_tensors='''pt'''					).to(lowerCAmelCase__					)

													# forward pass
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE       :   str         =      model(**lowerCAmelCase__					)

													# verify the logits
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Size((1, 10_00)					)
													self.assertEqual(outputs.logits.shape					,     lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.tensor([0.9996, 0.1966, -0.4386]					).to(lowerCAmelCase__					)
													self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     lowerCAmelCase__					,     atol=1e-4					)					)









'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''       ,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      MaskFormerConfig(backbone_config=A					)

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      847
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   List[str]         =      150
														SCREAMING_SNAKE_CASE       :   Any         =      '''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      171
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														SCREAMING_SNAKE_CASE       :   str         =      133
														SCREAMING_SNAKE_CASE       :   Any         =      '''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      19
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Tuple         =      65
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''mapillary-vistas-id2label.json'''

							SCREAMING_SNAKE_CASE       :   List[str]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}

							return config
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      []
							# stem
							# fmt: off
							rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')					)
							rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')					)
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')					)
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')					)
							# stages
							for i in range(len(config.backbone_config.depths					)					):
														for j in range(config.backbone_config.depths[i]					):
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

														if i < 3:
																					rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
														rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

							# FPN
							rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')					)
							for source_index, target_index in zip(range(3       ,		0       ,		-1					)       ,		range(0       ,		3					)					):
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
							rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')					)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers					):
														# self-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
														# cross-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
														# MLP 1
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
														# MLP 2
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
														# layernorm 3 (final layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')					)

							# heads on top
							rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')					)

							rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')					)

							rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')					)

							for i in range(3					):
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")					)
							# fmt: on

							return rename_keys
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dct.pop(A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      val
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
							for i in range(len(backbone_config.depths					)					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      num_features[i]
														for j in range(backbone_config.depths[i]					):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
																					SCREAMING_SNAKE_CASE       :   Any         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE       :   int         =      in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[-dim :]
																					# fmt: on
def        UpperCAmelCase (    A	:					Any       ,		A	:					Optional[Any]					):
							# fmt: off
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers					):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   int         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Dict         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
							# fmt: on
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Tuple         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE       :   Tuple         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					str       ,		A	:					bool = False					):
							SCREAMING_SNAKE_CASE       :   int         =      get_maskformer_config(A					)

							# load original state_dict
							with open(A       ,		'''rb'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      pickle.load(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      data['''model''']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      create_rename_keys(A					)
							for src, dest in rename_keys:
														rename_key(A       ,		A       ,		A					)
							read_in_swin_q_k_v(A       ,		config.backbone_config					)
							read_in_decoder_q_k_v(A       ,		A					)

							# update to torch tensors
							for key, value in state_dict.items():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.from_numpy(A					)

							# load 🤗 model
							SCREAMING_SNAKE_CASE       :   Any         =      MaskFormerForInstanceSegmentation(A					)
							model.eval()

							for name, param in model.named_parameters():
														print(A       ,		param.shape					)

							SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.load_state_dict(A       ,		strict=A					)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(A					) == 0, F"""Unexpected keys: {unexpected_keys}"""

							# verify results
							SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_img()
							if "vistas" in model_name:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      65
							elif "cityscapes" in model_name:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      65535
							else:
														SCREAMING_SNAKE_CASE       :   List[str]         =      255
							SCREAMING_SNAKE_CASE       :   Dict         =      True if '''ade''' in model_name else False
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      MaskFormerImageProcessor(ignore_index=A       ,		reduce_labels=A					)

							SCREAMING_SNAKE_CASE       :   List[str]         =      image_processor(A       ,		return_tensors='''pt'''					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model(**A					)

							print('''Logits:'''       ,		outputs.class_queries_logits[0, :3, :3]					)

							if model_name == "maskformer-swin-tiny-ade":
														SCREAMING_SNAKE_CASE       :   Any         =      torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,		A       ,		atol=1e-4					)
							print('''Looks ok!'''					)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
														Path(A					).mkdir(exist_ok=A					)
														model.save_pretrained(A					)
														image_processor.save_pretrained(A					)

							if push_to_hub:
														print('''Pushing model and image processor to the hub...'''					)
														model.push_to_hub(F"""nielsr/{model_name}"""					)
														image_processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='maskformer-swin-tiny-ade',
							    type=str,
							    help=('Name of the MaskFormer model you\'d like to convert',),
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
							    type=str,
							    help='Path to the original state dict (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							lowerCAmelCase_  :							List[Any]             =    parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)




